Real-Time Traffic State Measurement Using Autonomous Vehicles Open Data

Autonomous vehicle (AV) technologies are expected to disrupt the existing urban transportation systems. AVs’ multi-sensor system can generate large amount of data, often used for localization and safety purposes. This study proposes and demonstrates a practical framework for real-time measurement of local traffic states using LiDAR data from AVs. Fundamental traffic flow variables including volume, density, and speed are computed along with the traffic time-space diagrams. The framework is tested using the Waymo Open dataset. Results provide insights into the possibility of real-time traffic state estimation using AVs’ data for traffic operations and management applications.

• Record URL:
  • http://dx.doi.org/10.1109/OJITS.2023.3298893
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/26877813
• Supplemental Notes:
  • © 2023 The Authors.
• Authors:
  • Wang, Zhaohan
  • Keo, Profita
  • Saberi, Meead
• Publication Date: 2023

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 602-610
• Serial:
  • IEEE Open Journal of Intelligent Transportation Systems
  • Volume: 4
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 2687-7813
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Data fusion; Traffic flow; Traffic measurement
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Vehicles and Equipment;

Filing Info

• Accession Number: 01891339
• Record Type: Publication
• Files: TRIS
• Created Date: Aug 28 2023 9:19AM